Generally speaking, automatically detecting abnormal behavior in surveillance video is highly desirable. Automatic detection is difficult, however, because abnormal behavior is often based on contextual information and fails to conform to universal rules. Automatic detection is also difficult because of the large volume of video data that is available from video surveillance systems. For example, a modest video surveillance system with 10 cameras, each capturing video at a reduced frame rate of 5 frames per second (fps), captures over 4 million frames for analysis in a single day.
One type of abnormal behavior is movement in a direction different from a normal, preferred, and/or designated direction (i.e., counter-flow motion). For example, transportation hubs (e.g., airports, subways, etc.) may have designated directions for people to follow in certain areas (e.g., security checkpoints, entrances, exits, gates, etc.). Likewise, roads, driveways, and/or parking-lots/decks may have designated directions for vehicles to follow in certain areas (e.g., on/off ramps, loading zones, entrances/exits, etc.).
Existing solutions for automatically detecting counter-flow motion typically require a user to designate a flow direction (i.e., normal direction, main direction) for a particular area. In these solutions, the user must calibrate the system by assigning a flow direction. Once calibrated, the system cannot accommodate changes to the flow direction automatically and often can only accommodate a single flow direction for a given scene. A need, therefore, exists for a method to automatically determine a flow direction so that counter-flow motion may be detected automatically without user calibration.
Computer-vision algorithms for detecting counter-flow typically operate on images (i.e., frames) in a video stream (i.e., video). For example, particular features in the images may be identified and then tracked frame-to-frame in the video to determine motion. Because these algorithms operate on features defined by pixels, high-quality images (i.e., frames) are important. Most surveillance systems, however, compress video to improve storage, speed, and/or bandwidth, which reduces the quality of each frame. As a result, most computer vision algorithms must include additional operations associated with decoding the compressed video into its decompressed state before detecting motion. These added operations are computationally complex and time consuming, thereby slowing the process of automatically detecting counter-flow. The added operations, therefore, make detecting counter-flow in (near) real time unrealistic. An additional need, therefore, exists for a system and method to detect counter-flow motion using compressed video without any decompression operations.
Detecting counter-flow motion using compressed video may provide (near) real time detection of counter-flow motion. Real-time detection is highly desirable because it enables alarms to be activated as the counter-flow motion occurs. In addition, near real time detection can provide indications of counter-flow motion in a graphical user interface to allow a user to easily navigate to, and interact with, video that has counter-flow motion. These applications could influence safety and security applications significantly. Thus, an additional need exists for alarms based on (near) real-time detection of counter-flow movement. Further, an additional need also exists for a video management system (VMS) having a graphical user interface (GUI) that provides an indication of the presence and/or extent of counter-flow motion in a video and that facilitates easy navigation to, interaction with, and/or analysis of the portions of the video containing counter-flow motion.